Data Strategy

🚀 The Book in 3 Sentences

This book is about strategy, and data is the context in which strategy is discussed. There are some things like the McKinsey data maturity model that are discussed, but the main jist is the strategy. ‘Change is inevitable. … Change is constant.’ This is an important aspect of this entire book.

🎨 Impressions

I loved the quotes from this book, but I think I need to return to the book quotes and look back at them later. ‘To me, ideas are worth nothing unless executed. They are just a multiplier. Execution is worth millions.’ That is a super exciting quote.

Execution is to practice, as strategy is to theory.

✍️ My Top Quotes

Ancient Greek strategos, meaning the ‘art of troop leader;
In 2012 Cynthia Montgomery published a book anyone interested in strategy should know well as a great starting point to understand its purpose – The Strategist.
IBM has estimated that bad data costs US companies $3.1 trillion a year, equating to 12 per cent of revenue.
‘Data is great, but strategy is better!’ Steven Sinofsky
The data strategy should certainly cover the following from the ecosystem: Data management, as it is commonly termed, including structured and unstructured data, by which I mean: data standards; data architecture; data governance and quality; data integration and migration; data acquisition; data transformation and exchange (that is, with other parties, requiring typically a memorandum of understanding between both organisations); data compliance – including regulations and any other legal constraints – and corporate data security; data accessibility – providing appropriate access to the people who need it; master data management – which systems hold the primary version of data, and which leads in to the systems strategy, in terms of rationalising (potentially part of technology debt) or acquiring systems.
Data exploitation, which can be subdivided into: reporting and the provision of MI, especially key performance indicators (KPIs), usually involving dashboards and other visualisation techniques; analytics (descriptive, diagnostic, predictive and prescriptive), including the application of machine learning and AI (sometimes referred to as data science); insight – the gathering of additional information and data through research activities to fill gaps in understanding or test out approaches, and the benchmarking and baselining of activities and performance to determine comparative performance with other organisations conducting similar activities; knowledge management to garner real insight from a myriad of internal sources that are often unstructured and difficult to capture and/or harmonise to deliver coherent insight for the organisation in a structured form.
The data strategy will start the process of making people within your organisation recognise data as an asset
Ensure the data strategy is written in plain English (or whatever language it will be produced in), avoiding technical terms, ambiguity and acronyms and, most of all, keeping it simple.
It stated that research had identified that in the previous year 67 per cent of well-formulated strategies failed due to poor execution – two in three, which demonstrated there was clearly more chance of a strategy failing than it delivering.
Carucci identified a number of causes for such alarming failures in executive appointments and strategy execution: They lack depth in their competitive context. They are dishonest or naive about trade-offs. They leave old organizational designs in place. They can’t handle the emotional toll.
The first year Bridges conducted the survey, in 2002, 90 per cent of strategy implementations failed on the measure of achieving at least 50 per cent of the intended outcomes in the time set. In the 2012 survey, 80 per cent of business leaders felt their company was good at crafting strategy, but only 44 per cent saw similar capabilities in execution. Worse still, only 2 per cent believed the implementation would achieve 80–100 per cent of the objectives set.
Perversely, the survey found business leaders spent more time on strategy implementation than crafting the strategy itself, and 96 per cent that thought that their bonuses should be tied to successful strategy implementation.
Data, and its exploitation, sits at the heart of every business and is increasingly important as its capture becomes ever more complex as the channels through which it is made available and updated expand.
Organisations that want to exploit the potential of their data need a coherent way to ensure there is clarity on what is to be achieved, and this is where a data strategy comes in. What it often highlights, though, is that there is a lot of foundation work to be done to get the data into a state to exploit it fully, as fragmentation of data, poor data quality and missing or obsolete data are generally common barriers for all organisations. This in itself is useful, but can often feel like taking a giant stride backwards before being able to go forwards.
There are a number of challenges in positioning the data strategy, especially if this is the first time your organisation has embarked on having one, and it is important you are able to comprehend the environment that led to it being commissioned.
Identify who ‘owns’ the data strategy in your organisation, and ensure the expectations are clear and roles are defined.
Look to include the management of data and the exploitation of it within the data strategy if possible – this links the investment in the foundations to the potential value generated to the wider organisation.
Remember the CLEAR principles – clarity, leadership, execution, agility and relevancy – in preparation for embarking on defining the data strategy. These are key to making sure you are prepared and have evaluated fully your starting point and the direction in which you are heading.
‘Strategy is about making choices, trade-offs; it’s about deliberately choosing to be different.’
One of the challenges with developing a data strategy, particularly if this is the first time the organisation will have had one, is determining the scope.
At its simplest level, a data strategy is a vision for where you want to be that helps the organisation achieve its overall goals more efficiently and effectively.
Simply reviewing the key systems in the organisation to identify where data resides and cataloguing it is the starting point of a coherent approach to master data management, providing clarity on which sources of data are to be used (the master data), defining the metadata to underpin a common understanding of that data, and taking the first step to a data quality programme to improve quality and hence reliability.
It should go without saying that you have to deliver for the credibility of your sponsor to remain intact. Do not overpromise and under-deliver, and ensure the activity you undertake is time bound so you are able to demonstrate progress.
There are many organisations that invest time and effort in developing a strategy only for it to fail to make it to implementation – it becomes shelfware. The strategy may have been partly implemented, not as a strategic implementation, but simply because it made good sense to do so, and you should guard against finding fragments of the strategy and concluding it must have been implemented.
In some organisations the commitment to strategy is such that it has its own team, or even function. This might suggest that such an organisation is a strong advocate of defining and implementing strategy, but there is a common trap that occurs in such a situation, which I shall call the theorists dilemma. Often, the strategy team has been formed with the best of intentions, with the senior executives within the organisation being aware that there is a need for a strategy but not having the skills or the time to define one. The answer? To recruit and develop a strategy team to lead this important activity. The problem? It has instantly created a siloed approach in which strategy is a stand-alone function in the organisation.
The intent is fine but the execution has divorced it from the very people who need to define and implement the strategy and have the skin in the game to both make it realistic and enable it to be held to account. The
Data permeates every part of the organisation, so it is essential that all are included to some degree in the construction and approval of the data strategy.
The most common failures with data strategies are a lack of engagement, with too many people within the organisation being totally unaware the organisation even has a data strategy.
The strategy process is heavily focused on meeting that expectation, it is a good indicator that the organisation is doing this because of external pressure rather than because an internal driver has set the direction.
Assess the readiness and maturity of the organisation at the outset.
Identify the key drivers for the commissioning of the data strategy and adopt the appropriate response to navigate your way through to deliver something of value to the organisation.
Review the scope and reset if appropriate before you make a start. Be clear on what is expected and the timeline to achieve it.
Stakeholder engagement and communication will be key to avoid failure. Ensure you understand your starting point – what the stakeholders believe and expect of the data strategy, their commitment to it and how you maintain a dialogue.
Don’t discount quick wins to gain support through being opportunist. These may be imperfect in the longer term, but will build confidence and establish credibility if there is any doubt about the value a data strategy might bring to the organisation.
‘Strategists who don’t take time to think are just planners.’
No matter how polished you believe the data strategy is, you are not the target audience and so not the person who determines whether it is deemed a success.
Use yet another acronym to try to keep the evolving data strategy on track – PRIDE: purpose, relevance, inspiring, deliverable, enabling. This provides a useful way to consider the intent of the data strategy through its composition.
The purpose of creating a data strategy is apparently simple – to devise a data strategy to move the organisation forward in the broadest sense of data-related activities, whilst focusing on how this helps support the corporate strategy
The data strategy needs to be relevant to those who are intended to read it.
I described in earlier chapters how the data strategy needs to be an enabler of the corporate strategy, in terms of both providing a consistent thread through priorities as well as identifying those things that can raise the bar of the corporate strategy.
To succeed, the data strategy needs to become embedded as part of the culture of the organisation. This is evidenced by the organisation becoming more data-focused, information literate and confident in using evidence to make decisions, which in turn delivers benefits that can be directly linked back to the data strategy.
To get traction, remember it is about a RAVE. Without relevance, awareness, value and execution, the data strategy is incomplete and so will be lacking an essential component for a successful outcome.
Good plan is like a road map: it shows the final destination and usually the best way to get there.’
‘A good plan is like a road map: it shows the final destination and usually the best way to get there.’
Have previously made the point that there are two common failures with data strategies that lead to their becoming either shelfware or forgotten, and that is a lack of realism, or practical application to the current starting point, in terms of what it lays out as the vision to be delivered through the data strategy, and a lack of keeping it simple, or grounded in that reality.
I have previously made the point that there are two common failures with data strategies that lead to their becoming either shelfware or forgotten, and that is a lack of realism, or practical application to the current starting point, in terms of what it lays out as the vision to be delivered through the data strategy, and a lack of keeping it simple, or grounded in that reality.
There are some key elements you would expect to find in a data strategy: a strong data management vision; a coherent business case to support investment (even if that is just resources within the organisation, these still come at a cost); some guiding principles, values and alignment to the corporate vision; clearly articulated goals related to data; evaluation criteria and metrics to track success; clarity on the data strategy programme vision to be delivered; clarity of roles and responsibilities.
My recommendation is to contain the data strategy within 12–20 pages, and to focus on the high-level direction setting whilst providing waymarkers as a guide to drive the expected pace in the implementation phase.
The French mathematician Blaise Pascal famously wrote, ‘I have made this longer than usual because I have not had time to make it shorter.’
The starting point of a data strategy should be an assessment of where you are starting from as an organisation, and this applies in particular with the maturity of the organisation related to data management.
The final stage of the data lifecycle is to archive or, in most cases, destroy the data. In some (rare) cases, the data may not be destroyed due to the legal requirement to retain it or it being a matter for public record.
Most consulting firms use these, and there are certainly versions, like the McKinsey maturity model on analytic capability and utilisation,
The acid test should be whether the data strategy is coherent enough to be picked up and delivered in the way you intended, recognising there will always be a degree of latitude required as events unfold and time informs what were assumptions made earlier.
Strategy implementation is a high-risk activity; most fail.
‘You can have brilliant ideas, but if you can’t get them across, your ideas won’t get you anywhere.’ Lee Iacocca
The Herzberg model of motivational behaviours is an interesting perspective on what motivates people and what the hygiene factors are that must be right but are not, in themselves, motivational.
There are formal models to enable you to assess organisational maturity which look at benchmarks for process, quality, collaboration, knowledge, training and development, and change, amongst other things, depending on which model you choose to deploy. However, for the purposes of the data strategy, the requirement is less detailed
Culture and employee engagement are at the top of the list for programmatic failures worldwide, so do not underestimate this and prepare extensively for what you need to do to succeed.
‘All you need is the plan, the road map, and the courage to press on to your destination.’ Earl Nightingale
This found that a third of respondents at the best executing organisations identified implementation as a strategic activity in itself with lessons learnt fed back into the process, compared to just 11 per cent at other organisations. In addition, 59 per cent of those organisations who were best at execution involved staff who set the high-level strategy in its implementation, compared to just 23 per cent elsewhere.
‘several widely held beliefs that managers hold about how to implement strategy are just plain wrong’.
The five critical myths they focused on were: Execution equals alignment. Execution means sticking to the plan. Communication equals understanding. A performance culture drives execution. Execution should be driven from the top.
Waymarkers are guidelines to give the data strategy some context as to the intended course and pace of delivery but are not constraints or inflexible; their purpose is simply to assist in translating the breadth of the data strategy into something navigable and to give some insight into the intent of those who devised and delivered the strategy in the first place.
As W. Edwards Deming said: ‘Two basic rules of life are: 1) Change is inevitable. 2) Everybody resists change.’ Your challenge is to accept the first premise by taking the waymarkers and recognising the data strategy is not a fixed programme to be followed to the letter, whilst ensuring that the second rule does not apply to you, nor those you are collaborating with and dependent upon to ensure the implementation is a success.
Strategy execution fails not through the lack of clarity, but a lack of will to work together when it becomes reality. Therefore, it is imperative that you are challenging and retain a healthy dose of scepticism on the commitments of those you need to pull together on this until you see the behaviours translate into positive, collaborative outcomes.
‘There are two fatal errors that keep great projects from coming to life: Not finishing Not starting’
General George S. Patton, renowned for his dislike of indecision, stated: ‘A good plan, violently executed now, is better than a perfect plan next week.’
‘Uncertainty is an uncomfortable position. But certainty is an absurd one.’
One.’18 The implementation plan will translate the data strategy into a series of defined deliverables to achieve, via milestones, the waymarkers you outline within the data strategy. The plan will iterate and evolve, shaped by
Voltaire: ‘Uncertainty is an uncomfortable position. But certainty is an absurd one.’
The most effective organisations at strategy execution treat implementation as a strategic activity, with the same people involved in both definition and execution.
The mobilisation phase of your data strategy implementation is critical to success. It enables detail to be gathered to ensure readiness to begin implementation, establishing the data strategy baseline, assumptions and key drivers.
Avoid strategy paralysis. There is never a period in which total knowledge is captured, so accept it is a world of change and embrace it. As Voltaire said, certainty is an absurd position. Utilise Agile, engage with your sponsor and gather facts as you go to refine the inputs that shape the plan and its deliverables
A star chamber is a scrutiny board which challenges rigorously the thinking and decision-making process to test out how effective and optimal the proposal or outcome is likely to be. It usually consists of people with significant experience who have not been directly involved. It is named after the court that sat in the Old Palace of Westminster between the fifteenth and seventeenth centuries that was composed of judges and privy councillors.
2013. https://eiuperspectives.economist.com/strategy-leadership/why-good-strategies-fail
Project One, the management consulting firm, talk about the benefits of a full mobilisation approach in a blog post (https://projectone.com/blogs/mobilising-change-programme/) and as an article (https://projectone.com/mobilising-change-programmes-ive-started-so-ill-finish/
‘You have to be fast on your feet and adaptive or else a strategy is useless.’ Attributed to Charles de Gaulle
Mobilisation has given you the opportunity to review the continued relevance of the data strategy at a more detailed level, challenging the RAID log and enhancing it to provide the level of data you are going to need to be able to track an active RAID log as part of the implementation of the data strategy.
Accountability should never be shared
One final observation. The use of RACI within organisations does not mean that it is necessarily followed. This may seem a perverse statement: after all, the whole point is to assign roles and responsibilities.
If there is anything experienced programme managers will tell you about what made for successful programme delivery, one of their key messages would almost certainly be the need to focus your plan on what you intend to do, even if reality quickly becomes divergent from it. As Winston Churchill put it: ‘Those who plan do better than those who do not plan even though they rarely stick to their plan.’
‘Strategy must have continuity. It can’t be constantly reinvented,’ adding: ‘continuity of strategic direction and continuous improvement in how you do things are absolutely consistent with each other. In fact, they’re mutually reinforcing.’
Indeed, Masaaki Imai, regarded as the father of kaizen – defined as gradual, unending improvement, doing ‘little things’ better; setting, and achieving, ever higher standards – describes incremental change as a key part of continuous improvement. He says it is ‘not a paradigm shift or invention, but slow and steady progress is the most innovative.
Think about this carefully. Try to remember the last six to ten corporate messages you were told, set them down and try to identify the important ‘takeaway’ message in each. Do the same with some colleagues in different areas who should have had the same corporate messages.
Many strategy implementations fail as the communications all but dry up, starting off with gusto and then turning into a trickle some months down the line, till there is no communication being issued at all.
A study by Stieglitz (2012) found over 70 per cent of failed projects were due to a lack of requirements gathering.
Documented requirements have a breadth of detail encompassing all aspects – technical, functional, operational – and so tend to be created by those more closely associated with the technical detail who are focused on the delivery end of the activity, which can remove the level of engagement with the end stakeholder more closely associated with the user case.
Finally, I would like to highlight the importance of transparency in the reporting of benefits to ensure those who are engaged in some capacity
Jeff Austin, the former Vice-President, Strategy Planning, at DuPont Pioneer said: ‘Are we doing what we said we would be doing?’
Change is inevitable, so do not stick rigidly to the plan. Constant review is necessary, but if you find a need to diverge so far that it no longer relates to the data strategy as defined, you may need to redefine the data strategy.
Harvard Business Review and Strativity Group identified 62 per cent, the highest rate of failure, was due to poor communications. Referenced in S. Percy, Why Do Change Programmes Fail? Forbes. 13 March 2019. https://www.forbes.com/sites/sallypercy/2019/03/13/why-do-change-programs-fail/#112e91872e48
‘Change is inevitable. … Change is constant.’
The role of the sponsor is critical if and when you reach this point. The sponsor has not only potentially greater awareness of the wider political dimension as to what is driving the wider change decision, but also the network to be able to influence some of the audience you need to engage to enable you to gather all of your facts and get the right level of buy-in to the recommendations you put forward.
In some organisations, data is misleadingly put with the CIO on the grounds it resides in systems and so logically belongs to the CIO. Aside from the fact that not all data is in systems – unstructured data is often still paper-based,
In terms of data strategy, I have been an advocate of adopting a rolling view rather than operating to fixed points of time throughout my career.
‘To me, ideas are worth nothing unless executed. They are just a multiplier. Execution is worth millions.’
‘The measure of success is not whether you have a tough problem to deal with, but whether it is the same problem you had last year.’
Prior to commencing implementation, it is good practice to revisit the success criteria to evaluate whether these are still accurate, measurable and achievable in the time permitted.
The KAB model is also referred to as a social cognition model, or knowledge, attitude and practice (KAP) model.
Avoid embarking on a data strategy solely for compliance reasons.
Acquisitions – often the acquisition of one organisation by another will have a significant dependency on systems and data integration. Any delay in addressing these just continues to carry inefficiencies, resulting in a higher cost base and eradicating value through management time devoted to reconciling two organisations trying to operate as one.
The evolutionary approach is typical of most data strategy implementations. This is just as acceptable a way of proceeding with the data strategy implementation, as it can be executed alongside other changes which are managed through a dependency on other activity, thus reducing the scale of the programme to be managed directly by the implementation team.
The skills to move the data strategy forward in either definition or execution will depend entirely on the maturity of your organisation in the data arena.
Try to get senior leaders and a few others involved; don’t think you have to convey the message alone. As I have said a number of times in this book, recognise the importance of influencers within the group, the people who are recognised for talking sense and project credibility amongst their peers.
Like water; it is renewable and a source of life for your organisation.
Data is like water; it is renewable and a source of life for your organisation.
Resilience is key. You may have a project team, or it may be a singular endeavour. Either way, there will be ups and downs in the programme, and you need to prepare for these and keep others motivated when there are days that don’t go so well.
Finally, learn as you deliver. I am an advocate of rolling strategies, adjusting and reflecting as each year passes, using learning to adapt and evolve the strategy to suit business needs and the experience gained in strategy implementation